Name
Abstract | ||
|---|---|---|
Acknowledgments The completion of this thesis would not have been possible without Dr. Harris’s support and advice. Dr. Dascalu renewed my interest in user interaction, and his kind words of encouragement were much appreciated. Dr. Louis provided the original challenge, which has turned out to be harder than I expected, that led to the Ringermute project. I also appreciate the cooperation of Dr. Eubank, who is helping me finish yet another degree. iii Abstract This thesis presents Ringermute, an application designed to support audio feature recognition and machine learning, from the training and testing to the deployment phase. By choosing from a combination of feature extraction routines provided by plug-ins, a researcher can quickly produce files for input to standard data mining tools. The best combination of feature-extraction and classifier plugins can then be used to drive a near-real-time application for further testing or production use. iv Contents |
Year | Venue | Keywords |
|---|---|---|
2010 | CATA | data mining,near real time,feature extraction,feature recognition,machine learning |
Field | DocType | Citations |
Data mining,Data stream mining,Software deployment,Feature (computer vision),Computer science,Feature recognition,Feature extraction,Plug-in,Classifier (linguistics) | Conference | 0 |
PageRank | References | Authors |
0.34 | 50 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Levy | 1 | 0 | 0.34 |
| Sergiu Dascalu | 2 | 362 | 79.10 |
| Frederick C. Harris Jr. | 3 | 547 | 78.86 |